The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists.

The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists. Table of Contents: List of Figures / List of Tables / Narratological Background / Characters as Intentional Agents / Time / Plot / Summary and Future Directions

Narrative Intelligence (NI) — the confluence of narrative, Artificial Intelligence, and media studies — studies, models, and supports the human use of narrative to understand the world. This volume brings together established work and founding documents in Narrative Intelligence to form a common reference point for NI researchers, providing perspectives from computational linguistics, agent research, psychology, ethology, art, and media theory. It describes artificial agents with narratively structured behavior, agents that take part in stories and tours, systems that automatically generate stories, dramas, and documentaries, and systems that support people telling their own stories. It looks at how people use stories, the features of narrative that play a role in how people understand the world, and how human narrative ability may have evolved. It addresses meta-issues in NI: the history of the field, the stories AI researchers tell about their research, and the effects those stories have on the things they discover. (Series B)

From literature and film to advertisements, storytelling is an important aspect of daily life. To create an impactful story, it is important to analyze the creation and generation of a storyline. Content Generation Through Narrative Communication and Simulation is a critical research publication that explores story and the application of story in various forms of media as well as the challenges of automated story. Featuring coverage on a wide range of topics such as narrative or story generation systems, the film and movie narrative generation, and narrative evaluation, this book is geared toward researchers, students, and professionals seeking current and relevant research on the influence and creation of story in media.

Event structures are central in Linguistics and Artificial Intelligence research: people can easily refer to changes in the world, identify their participants, distinguish relevant information, and have expectations of what can happen next. Part of this process is based on mechanisms similar to narratives, which are at the heart of information sharing. But it remains difficult to automatically detect events or automatically construct stories from such event representations. This book explores how to handle today's massive news streams and provides multidimensional, multimodal, and distributed approaches, like automated deep learning, to capture events and narrative structures involved in a 'story'. This overview of the current state-of-the-art on event extraction, temporal and casual relations, and storyline extraction aims to establish a new multidisciplinary research community with a common terminology and research agenda. Graduate students and researchers in natural language processing, computational linguistics, and media studies will benefit from this book.

Although both deal with narratives, the two disciplines of Narrative Theory (NT) and Computational Story Composition (CSC) rarely exchange insights and ideas or engage in collaborative research. The former has its roots in the humanities, and attempts to analyze literary texts to derive an understanding of the concept of narrative. The latter is in the domain of Artificial Intelligence, and investigates the autonomous composition of fictional narratives in a way that could be deemed creative. The two disciplines employ different research methodologies at contradistinct levels of abstraction, making simultaneous research difficult, while a close exchange between the two disciplines would undoubtedly be desirable, not least because of the complementary approach to their object of study. This book, From Narratology to Computational Story Composition and Back, describes an exploratory study in generative modeling, a research methodology proposed to address the methodological differences between the two disciplines and allow for simultaneous NT and CSC research. It demonstrates how implementing narratological theories as computational, generative models can lead to insights for NT, and how grounding computational representations of narrative in NT can help CSC systems to take over creative responsibilities. It is the interplay of these two strands that underscores the feasibility and utility of generative modeling. The book is divided into 6 chapters: an introduction, followed by chapters on plot, fictional characters, plot quality estimation, and computational creativity, wrapped up by a conclusion. The book will be of interest to all those working in the fields of narrative theory and computational creativity.